plot_results
method plot_results(models=None, metric=None, title=None, legend="lower right", figsize=None, filename=None, display=True)[source]
Plot the model results.
If all models applied bootstrap, the plot is a boxplot. If
not, the plot is a barplot. Models are ordered based on
their score from the top down. The score is either the
score_bootstrap or score_test attribute of the model,
selected in that order.
| Parameters | models: int, str, Model, slice, sequence or None, default=None
Models to plot. If None, all models are selected.
metric: int, str, sequence or None, default=None
Metric to plot (only for multi-metric runs). Other available
options are "time_bo", "time_fit", "time_bootstrap" and
"time". If str, add title: str, dict or None, default=None+ between options to select more than
one. If None, the metric used to run the pipeline is selected.
Title for the plot.
legend: str, dict or None, default="lower right"
Legend for the plot. See the user guide for
an extended description of the choices.
figsize: tuple or None, default=None
Figure's size in pixels, format as (x, y). If None, it
adapts the size to the number of models.
filename: str or None, default=None
Save the plot using this name. Use "auto" for automatic
naming. The type of the file depends on the provided name
(.html, .png, .pdf, etc...). If display: bool or None, default=Truefilename has no file type,
the plot is saved as html. If None, the plot is not saved.
Whether to render the plot. If None, it returns the figure.
|
| Returns | go.Figure or None
Plot object. Only returned if display=None.
|
See Also
Plot a model's confusion matrix.
Plot the probability distribution of the target classes.
Plot metric performances against threshold values.
Example
>>> from atom import ATOMClassifier
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_samples=1000, flip_y=0.2, random_state=1)
>>> atom = ATOMClassifier(X, y, random_state=1)
>>> atom.run(["GNB", "LR", "RF", "LGB"], metric=["f1", "recall"])
>>> atom.plot_results()
>>> atom.run(["GNB", "LR", "RF", "LGB"], metric=["f1", "recall"], n_bootstrap=5)
>>> atom.plot_results()
>>> atom.plot_results(metric="time_fit+time")